Analytical quality by design based on knowledge organization: A case study of developing an ultrahigh-performance liquid chromatography method for the detection of phenolic compounds.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Phytochemical Analysis Pub Date : 2024-07-01 Epub Date: 2024-03-10 DOI:10.1002/pca.3343
Yanni Tai, Mintong Zhao, Feng Ding, Gelin Wu, Haibin Qu, Ping Gong, Yongjian Xie, Peng Zhou, Xingchu Gong
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Abstract

Introduction: Despite numerous successful cases, there are still some challenges in using analytical quality by design (AQbD) for the development of analytical methods. Knowledge organization helps to enhance the objectivity of risk assessment, reduce the number of preliminary exploratory experiments, identify potential critical method parameters (CMPs) and their scope.

Objective: In the present study, we aimed to develop a simple, rapid, and robust analytical method for detecting phenolic compounds in Xiaochaihu capsule intermediates utilizing knowledge organization.

Methods: Knowledge organization and AQbD were combined to obtain the initial analytical conditions through knowledge collection, extraction, reorganization, and analysis. The quantitative relationship between critical method attributes (CMAs) and CMPs was then established by a definitive screening design. The method operable design region was calculated using an exhaustive Monte Carlo approach based on the probability of reaching the standard. Robustness investigation and methodological validation were finally performed.

Results: Analytical target profiles, CMAs, potential CMPs, and initial analytical conditions were initially identified, and the optimized ranges of operating parameters were obtained. A UHPLC method was successfully established for the analysis of phenolic compounds in ginger-ginger pinellia percolate, and the method validation outcomes were also satisfactory.

Conclusion: The developed method can be a reliable means to detect the phenolic compounds of Xiaochaihu capsule intermediates. Knowledge organization provides a new approach for making better use of prior knowledge, significantly enhancing the efficiency of analytical method development. The approach is versatile and can be similarly applied to the development of other methods.

基于知识组织的分析质量设计:开发超高效液相色谱法检测酚类化合物的案例研究。
导言:尽管有许多成功的案例,但在使用分析质量源于设计(AQbD)来开发分析方法方面仍然存在一些挑战。知识组织有助于提高风险评估的客观性,减少初步探索性实验的数量,确定潜在的关键方法参数(CMP)及其范围:本研究旨在利用知识组织开发一种简单、快速、稳健的分析方法,用于检测小柴胡胶囊中间体中的酚类化合物:方法:将知识组织与AQbD相结合,通过知识收集、提取、重组和分析,获得初始分析条件。然后通过确定性筛选设计建立关键方法属性(CMAs)与 CMPs 之间的定量关系。根据达到标准的概率,采用详尽的蒙特卡洛方法计算出方法可操作设计区域。最后进行了稳健性调查和方法验证:结果:初步确定了分析目标曲线、CMA、潜在 CMP 和初始分析条件,并获得了优化的操作参数范围。结果:初步确定了分析目标曲线、CMA、潜在的CMPs和初始分析条件,并获得了优化的操作参数范围,成功地建立了一种超高效液相色谱法来分析姜姜半夏中的酚类化合物,方法验证结果令人满意:结论:所建立的方法是检测小柴胡胶囊中间体酚类化合物的可靠手段。知识组织为更好地利用已有知识提供了一种新方法,大大提高了分析方法开发的效率。该方法用途广泛,可用于其他方法的开发。
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来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
审稿时长
1.7 months
期刊介绍: Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.
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